List hyperparameter tuning methods
Company: Amazon
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: easy
Interview Round: Technical Screen
Describe common methods for **hyperparameter tuning** in machine learning.
For each method, explain:
- How it works conceptually.
- Its advantages and disadvantages (e.g., efficiency, ease of parallelization, sample efficiency).
Include at least: manual search, grid search, random search, and more advanced methods such as Bayesian optimization or adaptive schemes.
Quick Answer: This question evaluates understanding of hyperparameter tuning methods, including the competencies to compare different search strategies, analyze trade-offs in efficiency and parallelization, and design experiments to optimize model performance.
